Mobile data collection and analysis with local differential privacy

Ninghui Li, Qingqing Ye

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

8 Citations (Scopus)


Local Differential Privacy (LDP), where each user perturbs her data locally before sending to an untrusted party, is a new and promising privacy-preserving model for mobile data collection and analysis. LDP has been deployed in many real products recently by several major software and Internet companies, including Google, Apple and Microsoft. This seminar talk first introduces the rationale of LDP model behind these deployed systems to collect and analyze usage data privately, then surveys the current research landscape in LDP, and finally identifies several open problems and research directions in this community.

Original languageEnglish
Title of host publicationProceedings - 2019 20th International Conference on Mobile Data Management, MDM 2019
PublisherInstitute of Electrical and Electronics Engineers Inc.
Number of pages4
ISBN (Electronic)9781728133638
Publication statusPublished - Jun 2019
Externally publishedYes
Event20th International Conference on Mobile Data Management, MDM 2019 - Hong Kong, Hong Kong
Duration: 10 Jun 201913 Jun 2019

Publication series

NameProceedings - IEEE International Conference on Mobile Data Management
ISSN (Print)1551-6245


Conference20th International Conference on Mobile Data Management, MDM 2019
Country/TerritoryHong Kong
CityHong Kong


  • Data analysis
  • Local differential privacy
  • Mobile data collection
  • Privacy

ASJC Scopus subject areas

  • Engineering(all)

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